Humana interview questions

1. 1 Are you familiar with Jira/Azure/ADO and working a project within them? (Also, technical questions like experience with APIs and backend integrations are looked for, though this can vary by team). Reported at Humana
2. 2 Define a practical metric framework for judging whether AI features create user value, product impact, and business return. Reported at Humana
3. 3 Describe a framework for prioritizing AI product features based on user value, feasibility, evaluation quality, and trade-offs. Reported at Humana
4. 4 Discuss the main ethical risks in deploying generative AI, including hallucination, misuse, privacy, and governance. Reported at Humana
5. 5 Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example. Reported at Humana
6. 6 Explain how you translate complex AI concepts for non-technical stakeholders so they can make clear decisions and stay aligned. Reported at Humana
7. 7 Are you familiar with RLHF (Reinforcement Learning from Human Feedback)? Typical for the role
8. 8 Can you tell me about a time you had to manage stakeholder expectations when an AI feature didn't perform as well as anticipated? Typical for the role
9. 9 Describe a situation where you had to pivot an AI product strategy based on technical limitations or model performance issues. How did you handle it? Typical for the role
10. 10 Describe your strategy for building a roadmap that balances model improvements with feature development and user experience enhancements. Typical for the role
11. 11 Design an AI feature for our product. Typical for the role
12. 12 Explain embeddings to a non-technical executive in under three minutes. Typical for the role
13. 13 Give me an example of a time you identified an opportunity to solve a customer problem using AI that others hadn't recognized. Typical for the role
14. 14 How do you approach conducting user research and validation for AI features where the technology might be unfamiliar to users? Typical for the role
15. 15 How do you approach GenAI safety in consumer products? Typical for the role
16. 16 How do you ensure your AI models are ethical and unbiased? Typical for the role
17. 17 How do you evaluate a prompt? Typical for the role
18. 18 How do you keep up to date with AI trends? Typical for the role
19. 19 How do you write a PRD (Product Requirements Document) for a feature whose output is probabilistic and occasionally wrong? Typical for the role
20. 20 How would you explain a complex AI system to a non-technical stakeholder and get buy-in? Typical for the role
22. 21 If user research shows customers want an AI feature that your data science team says is technically unfeasible with current resources, how do you proceed? Typical for the role
23. 22 Pick an AI product you admire. Typical for the role
24. 23 Share an example of when you had to prioritize between improving AI model accuracy and shipping a feature faster. How did you make the decision? Typical for the role
25. 24 Tell me a time when you had to work with models that were producing inconsistent predictions. How did you work with the scientists to address them? Typical for the role
26. 25 Tell me about a time you launched an AI-powered product or feature. What was your process from ideation through launch, and what did you learn? Typical for the role
27. 26 Tell me about a time you used data or experimentation to drive a decision in a high-ambiguity environment. Typical for the role
28. 27 Tell me about an AI-powered feature you launched where the model had significant technical limitations. How did you design the CX to manage user expectations and maintain trust? Typical for the role
29. 28 Tell me what you know about DPO (Direct Preference Optimization). Typical for the role
30. 29 Walk me through how you decide whether a problem needs AI at all, versus a rules engine or plain software. Typical for the role
31. 30 Walk me through how you would evaluate whether AI is the right solution for a given product problem versus a traditional approach. Typical for the role
32. 31 Walk me through the unit economics of an LLM feature. How do cost per inference and p95 latency change what you build? Typical for the role
33. 32 What are the essential differences between NN (Neural Networks) vs LLM (Large Language Models)? Typical for the role
34. 33 What are the strengths and weaknesses of classic ML models and the implications for the product? Typical for the role
35. 34 What's your approach to competitive analysis for AI products? Typical for the role
36. 35 What's your criteria in selecting a model? Typical for the role
37. 36 What's your framework for defining success metrics for AI-powered products? How do they differ from traditional product metrics? Typical for the role
38. 37 What's your understanding of Generative AI? Typical for the role
39. 38 What's your understanding of the RAG (Retrieval-Augmented Generation) framework? Typical for the role
40. 39 When would you reach for a rules-based system, when for a classic ML model, and when for an LLM? Typical for the role

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